293-8 Building Computational Workforce Skills Through a Shared Technical Training Ecosystem

Session: Developing and Assessing Workforce Skills in Geoscience Courses and Programs

Presenting Author:

Tammy Bravo

Authors:

Bravo, Tammy¹, Haberli, Gillian², Weber, Melissa³, Wilson, Sarah⁴, Weekly, Robert⁵, Parafina, Sophia⁶, Ulakovich, Austin⁷, Hubenthal, Michael⁸, Trabant, Chad⁹

(1) EarthScope Consortium, Washington, DC, USA, (2) EarthScope Consortium, Washington, DC, USA, (3) EarthScope Consortium, Washington, DC, USA, (4) EarthScope Consortium, Washington, DC, USA, (5) EarthScope Consortium, Washington, DC, USA, (6) EarthScope Consortium, Washington, DC, USA, (7) EarthScope Consortium, Washington, DC, USA, (8) EarthScope Consortium, Washington, DC, USA, (9) EarthScope Consortium, Washington, DC, USA,

Abstract:

Scientific computing skills are becoming essential across the geosciences, yet many learners struggle with barriers unrelated to the science itself, including software installation, computing environments, unfamiliar workflows, and limited programming experience. These challenges can reduce learner confidence and limit the development of workforce-ready skills.

The NSF National Geophysical Facility (NGF)'s Computing & Data Science Academy was established to reduce these barriers through a coordinated technical training program that combines shared cloud infrastructure, intentional instructional design, and community-developed courses. Rather than treating workshops as standalone events, the Academy provides a common instructional ecosystem that leverages a standardized cloud computing environment (GeoLab), reusable instructional resources, digital credentials, and learning pathways that support learners' progression from foundational computing skills to advanced scientific workflows.

To understand how these design choices support workforce development, the Academy is implementing a program-wide evaluation framework that examines technical readiness, learner confidence, participation, and continued engagement across multiple courses. Common learning outcomes and evaluation instruments support comparisons across courses while informing continuous improvement of curriculum and learner support. Early evaluation results suggest improvements in learner confidence and perceived readiness to apply computational workflows in research.

This presentation describes the Academy's instructional framework and emerging assessment strategy for scalable workforce development in computational geoscience. We discuss how shared infrastructure, intentional instructional design, and coordinated evaluation can strengthen workforce preparation while providing a model that can be adapted across geoscience education programs.

Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-20558

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